title: Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces.
abstract: Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, that need to be tackled to achieve efficient performance of such systems. For instance, any restorative BCI needs an optimum trade-off between time window length, classification accuracy and classifier update rate. In this study, we have investigated possible solutions to these problems by using a dataset provided by the University of Graz, Austria. We have used a continuous wavelet transform and the Student t-test for feature extraction and a support vector machine (SVM) for classification. We find that improved results, for restorative BCIs for rehabilitation, may be achieved by using a 750 milliseconds time window with an average classification accuracy of 67% that updates every 32 milliseconds.
authors.forename: Sam
authors.initials: S
authors.lastname: Darvishi
authors.forename: Michael C
authors.initials: MC
authors.lastname: Ridding
authors.forename: Derek
authors.initials: D
authors.lastname: Abbott
authors.forename: Mathias
authors.initials: M
authors.lastname: Baumert
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D006801:Humans; D060388:Support Vector Machine; D013997:Time Factors; D058067:Wavelet Analysis